Systems and methods for motion correction in positron emission tomography imaging

ABSTRACT

Systems and methods for compensating motion artifacts in positron emission tomography (“PET”) imaging based on medical images acquired with a medical imaging system are provided. In some embodiments, the method includes acquiring PET data from a subject with a PET system during which at least a portion of the subject is undergoing motion, and providing medical images acquired from the subject using a medical imaging system, the medical images including regions depicting motion. The method also includes estimating, from the medical images, motion information associated with the motion of the at least a portion of the subject, and reconstructing a motion-corrected PET image using the PET data using a reconstruction algorithm that incorporates the motion information into a system matrix.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 61/821,761, filed on May 10, 2013, and entitled“Compensation for External and Internal Motion in PET-MR.”

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under EB012326 awardedby the National Institutes of Health. The government has certain rightsin the invention.

BACKGROUND OF THE INVENTION

The field of the invention is systems and methods for medical andmolecular imaging. More particularly, the invention relates to systemsand methods for motion correction in positron emission tomography(“PET”) imaging.

Positrons are positively charged electrons that are emitted byradionuclides that have been prepared using a cyclotron or other device.These are employed as radioactive tracers called “radiopharmaceuticals”by incorporating them into substances, such as glucose or carbondioxide. The radiopharmaceuticals are administered to a patient andbecome involved in biochemical or physiological processes such as bloodflow, fatty acid and glucose metabolism; and protein synthesis.

Some common clinical applications of PET imaging include oncology andcardiology for detecting and staging of cancer and cardiac diseases, aswell as for monitoring treatment response. In a PET imaging scan, aradioactive tracer that emits positrons is administered to the body of apatient. The released positrons immediately annihilate, creating photonpairs with 511 keV energies that propagate in opposite directions fromthe annihilation point. The volume distribution and concentration ofradioactive tracers in the body is determined based on the detection ofradiation outside the patient.

Generally, a PET scanner includes one or more rings of detectors thatencircle the patient and convert the energy of each 511 keV photon intoa flash of light that is sensed by a radiation detector, such as aphotomultiplier tube (“PMT”). Coincidence detection circuits connectedto the radiation detectors record only those photons that are detectedsimultaneously by two detectors located on opposite sides of thepatient. The number of such simultaneous events indicates the number ofpositron annihilations that occurred along a virtual line joining thetwo opposing detectors, called the line of response (“LOR”). An imageindicative of the tissue concentration of the positron emittingradionuclide is created by determining the number of such annihilationevents at each location within the field-of-view.

Motion artifacts commonly found in PET images are mainly due toirregular motion, as well as periodic internal motion from respiratoryand cardiac activity. In particular, subject motion is generallydifficult to avoid, due to the long scan durations (up to severalminutes) necessary for PET imaging to be of clinical value, leading toimage degradation, or blurring, and severe artifacts when motion haslarge amplitudes. On the other hand, physiological activity causesorgans, such as heart muscle, lung, or abdominal organs, to changelocation, shape, or local tissue density, resulting in complex,non-rigid movement patterns. These effects limit the spatial resolutionthat can be achieved in PET imaging much more than physical factors,such as detector size, photon non-collinearity and positron range oftravel. For instance, the physical factors affecting spatial resolutiongenerally contribute to a deterioration of spatial resolution on theorder of 1-3 mm, as compared to 5-15 mm due to organ motion fromrespiratory activity. Clinical situations when motion may becomeimportant include, for example, small perfusion defects present in themyocardium and small liver, or lung tumors, which generally are notdetectable, or are much less visible, on images including motionartifacts. Therefore, absent motion corrections may result in differentdiagnostic outcomes.

Many approaches have been explored in the effort to correct motionartifacts. Depending on whether the motion is estimated from theacquired PET data or by other instrumentation, the approaches can bedivided into two groups: auto-correction and assisted-correction. Forthe auto-correction techniques, the measured PET data are divided intotemporal frames, or gates, and the motion is then estimated betweentemporal frames from the PET data. The estimated motion field can thenbe used to transform the reconstructed images or the sinograms of eachtemporal frame to a reference frame. The accuracy of motion estimationusing this approach is limited by the noise of PET images, whichincreases as the data set is divided into temporal frames for a dynamicimage sequence. Moreover, the fact that the motion estimation relies onthe generation of images or sinograms limits its temporal resolution.Thus, such methods are not suitable when the activity distribution isfast changing or the object is fast moving. For example, cardiac imagingof rapid dynamic functions, such as myocardial blood flow, may not bepossible using a gated approach due to the substantial noise associatedwith rejecting a large number of detected events in low count frames.The reconstruction algorithms of the assisted-correction approaches aresimilar to auto-correction techniques except that the motion informationis instead measured using an instrument other than the PET camera, suchas video/infrared cameras, and approaches with structured light.

Therefore, given at least the above, there is a need for systems andmethods that correct for internal and external motion artifacts commonlypresent in PET imaging.

SUMMARY OF THE INVENTION

The present invention overcomes the aforementioned drawbacks byproviding systems and methods directed to compensation of motionartifacts in positron emission tomography (“PET”) imaging. Inparticular, the present disclosure provides a robust and accuratemethodology for correcting PET acquisitions by identifying external andinternal motion patterns from medical images from a subject, suchmagnetic resonance (“MR”) or computed tomography (“CT”) images, andincorporating the determined motion into the PET image reconstructionprocess.

In one aspect of the present invention a method for compensating motionartifacts in positron emission tomography (“PET”) imaging based onmedical images acquired with a medical imaging system is provided. Themethod includes acquiring PET data from a subject with a PET systemduring which at least a portion of the subject is undergoing motion, andproviding medical images acquired from the subject using a medicalimaging system, the medical images including regions depicting motion.The method also includes estimating, from the medical images, motioninformation associated with the motion of the at least a portion of thesubject, and reconstructing a motion-corrected PET image using the PETdata and a reconstruction algorithm that incorporates the motioninformation into a system matrix.

The foregoing and other aspects and advantages of the invention willappear from the following description. In the description, reference ismade to the accompanying drawings which form a part hereof, and in whichthere is shown by way of illustration a preferred embodiment of theinvention. Such embodiment does not necessarily represent the full scopeof the invention, however, and reference is made therefore to the claimsand herein for interpreting the scope of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart setting forth the steps of a process forcorrecting PET image data in accordance with the present disclosure.

FIG. 2 is a pictorial view of a cross-section of a combination positronemission tomography (PET) imaging system and magnetic resonance imaging(MRI) system which employs the present invention.

FIG. 3 is a schematic diagram of the PET imaging system portion of thesystem of FIG. 2.

FIG. 4 is a schematic diagram of the MRI system portion of the system ofFIG. 2.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure is directed to systems and methods forcompensating motion artifacts present in positron emission tomography(“PET”) imaging using motion pattern information determined from medicalimages, such as magnetic resonance (“MR”) images or computed tomography(“CT”) images, acquired from a subject while at least a portion of thesubject is undergoing motion. As will be described, such motioninformation may be incorporated into the reconstruction process of PETdata, which significantly improves the potential quality andquantitative accuracy of the reconstructed PET images. As such, thepresent disclosure provides a methodology for correcting cardiac,respiratory, and other motion artifacts that may otherwise interferewith the visibility, as well as the accuracy, of structures depicted inPET images.

The approach of the present disclosure allows for significantly improvedPET spatial resolution by removing image blurring due to motion. Inaddition, image noise may be significantly reduced because all PETevents generated during acquisition may be utilized to generate animage, as opposed to previous reconstruction methods that only use afraction of events to provide an image consistent with a particularmotion state. Artifacts in PET imaging due to motion mismatches betweenthe emission and attenuation datasets may also be removed usingmethodologies described by the present disclosure. Furthermore, theinherent spatial non-uniformity of image properties may be reduced,including bias and resolution due to motion-based spatially variantregularizer present in the reconstruction algorithm.

Referring specifically to FIG. 1, a process 100 for correcting PET imagedata in accordance with the present disclosure is shown. The process 100may begin at process block 102 where PET data may be acquired from asubject using a PET system while at least a portion of a subject isundergoing motion. As described, such motion may include internal motiondue to physiological activity, such as cardiac or respiration activity,or external, aperiodic motion. At process block 104 medical imagesacquired from the subject using a medical imaging system, such as a MRsystem or a CT system, may be provided for purposes including motioncorrection.

In some embodiments, the PET system and medical imaging system may beintegrated into a combined imaging system, such as a PET-MR system, or aPET-CT system. As illustrated in FIG. 1, process blocks 102 and 104 maythen occur substantially contemporaneously. That is, PET data may beacquired substantially contemporaneously with acquisition of the medicalimaging data using a combined imaging system. For example, both the MRdata and PET data receive a time stamp when they are acquired such thatan MR signal acquired at substantially the same instance in time as aPET signal is employed to correct for motion at that moment in time. Assuch, simultaneous acquisition of PET and MRI allows to estimatecontinuously, for example, organ motion from MR imaging sequences orspecialized MR pulse sequences. In other embodiments, the medicalimaging system may be separate from the PET system, including a MR or aCT imaging system. Therefore, medical imaging data may be acquiredsequentially, contemporaneously, or in an interleaved manner, withrespect to PET data acquisition.

The acquired medical images may include motion effects, or motionartifacts, resulting from free breathing, or breath-held, pattern,cardiac activity, or other movement of the subject. In some aspects, themedical images may include MR images, acquired using any imagingsequences suitable for estimating motion fields. In other aspects, themedical images may include CT images, for example, acquired over asufficiently long period of time so as to allow estimation of internaland external motion.

As one non-limiting example, the medical images acquired at step 104 canbe acquired by directing an MRI system to perform MR tagging, whereby aspatially periodic magnetization pattern is induced in a subject using acombination of radio frequency pulses (“RF”) and field gradient pulses.The evolution of the magnetization pattern, in dependence of motionfactors, may then be monitored via the acquisition of subsequent MRimages.

As another non-limiting example, the medical images acquired at step 104can be acquired by directing an MRI system to perform a navigated pulsesequence, such as one that utilizes a steady-state free-precession(“SSFP”) acquisition interleaved with the acquisition of pencil-beamnavigator echo signals. In some configurations, navigator echo signalsmay be generated using two-dimensional RF excitation pulses thatselectively excite circular regions along an image plane and uniformlyalong an axis perpendicular to the image plane. In this manner, alongitudinal cylinder of tissue through, say a diaphragm dome, can beacquired for use in tracking target locations, markers, or anatomicallandmarks, such as a lung-liver interface. The position of such targetlocations can be extracted from the navigator profile using, forexample, an edge detection algorithm. Other types of navigators couldalso be implemented to measure subject motion, including circularnavigators and cloverleaf navigators. In addition, other specializedpulse sequences that provide direct measures of subject motion may alsobe utilized, including velocity-encoded pulse sequences.

Next, at process block 106, motion information may be estimated from themedical images provided at process block 104. In some embodiments,non-rigid motion may be derived from the medical images, in contrast toprevious methods, which typically estimate only rigid body motion. Asdescribed, such medical images may be generated using MR imagingsequences intrinsically sensitive to motion. Specifically with respectto MR tagging, spatial modulation of magnetization resulting fromapplication of an MR tagging sequence creates a periodic modulation ofmagnetization in space by exciting (and subsequently dephasing) aninterference pattern in the transverse magnetization due to a pair ofrectangular hard RF pulses separated in time by a gradient field pulse.This periodic magnetization pattern is distorted with movement of theimaged tissue, and therefore indicates positional changes occurringbetween the tagging preparation pulses and image acquisition that may beutilized to estimate motion.

In some embodiments, motion information may be estimated at processblock 106 based on MR navigator data. For example, respiratory motionmay be determined by way of tracking an anatomical location, such as alung-liver interface, during the respiratory cycle. This motion can betracked using navigator signals collected prior to each sliceacquisition to monitor the respiratory phase of that slice. Internalmotion information obtained in this manner allows accurate monitoring ofthe respiratory cycle and handling of respiratory cycle irregularities.To cover a maximum number of respiratory positions for each slice, eachsingle-slice steady-state free-precession acquisition may be repeated anumber of times. After a given slice has been acquired, a differentslice may be acquired using the same procedure, until an entire volumeof interest has been covered.

In order to estimate motion information at process block 106 a B-splinenon-rigid image registration approach may be used, which includessimilarity measures, such as the sum of the squared difference (“SSD”)and mutual information (“MI”), and a constraint term for motion fieldregularization. Specifically, motion fields may be estimated byminimizing the following cost function:

Φ=M(f _(T) ,Tf _(S))+βR(T)  (1);

where f_(T) and f_(S) are the target and source image respectively; T isa motion, or time warp, operator; M is a similarity measure; R(T) is aregularizer; and β is a regularization parameter. Because motionestimation is generally an ill-posed inverse problem, regularization isused in Eqn. (1) to achieve a stable and realistic solution. Requiringthe estimated motion to be invertible (i.e., the determinant of Jacobianof motion must be positive) has been shown to regularize the motionestimation problem. A simple regularizer that penalizes the differenceof the adjacent B-spline coefficients can be used. The regularizationparameter, β, can be increased until all Jacobian determinant values atall voxels are positive. This regularization parameter can be applied toboth B-spline SSD and MI based motion estimation algorithms.

In some embodiments, cubic B-spline interpolation can be used for imagesand estimated motion fields. For motion fields, B-spline knots(coefficients) can be located with a spacing of, say, 4 pixels in eachx, y, z direction. Local minima problem can be avoided using a bi-levelmultiresolution scheme. In particular, motion fields may be estimatedbetween adjacent phases and B-spline interpolation may be used for thecomposition of motion fields to estimate motions between differentphases other than adjacent phases.

As one example, noting f (t,x) as a medical image volume at a givenrespiratory phase, t, motion information can be estimated using aregistration algorithm that searches for an optimal three-dimensionalcubic B-splice motion field ĝ(t→t′, x) between a pair of volumes f (t,x)and f (t′,x), such that,

$\begin{matrix}{{{\hat{g}\left( {\left. t\rightarrow t^{\prime} \right.,x} \right)} = {\arg \; {\min\limits_{g}\left\{ {{\frac{1}{N}{\sum\limits_{x}^{\;}\; \left( {{f\left( {t,{g\left( {\left. t\rightarrow t^{\prime} \right.,x} \right)}} \right)} - {f\left( {t^{\prime},x} \right)}} \right)^{2}}} + {\beta \; {R\left( {g\left( {\left. t\rightarrow t^{\prime} \right.,x} \right)} \right)}}} \right\}}}};} & (2)\end{matrix}$

where x is the voxel position, N is the total number of voxels in oneimage volume, β is the regularization parameter, and R(•) is theregularizer. The regularization term penalizes the differences betweenadjacent B-spline coefficients and imposes local invertibility. Asmentioned above, a bi-level multiresolution strategy can be used toincrease the robustness and speed of the registration algorithm.

Once the motion information is estimated at process block 106, themotion information may be used to simultaneously reconstruct a PET imagewhile compensating for the effects of the subject motion, as indicatedat step 108. In particular, this result can be achieved by incorporatingthe motion information directly into the system matrix utilized in thePET reconstruction algorithm. In particular, this result can be achievedby incorporating the motion information directly into the system matrixutilized in the PET reconstruction algorithm. As one example, themotion-dependent system matrix can have the following form:

P _(t) =NBA _(t) GM _(t)  (3);

where M_(t) is a nonrigid warping operator that registers a given phaset to a reference phase, and which is based on the estimated motioninformation; G is the forward-projection operator; A_(t) is a diagonalmatrix containing the LOR attenuation correction factors for each frame,t; B models the point spread function (“PSF”) blurring effects in theprojection space; and N is the diagonal matrix of detector normalizationfactors.

Incorporating the motion information into the system matrix utilized inthe PET image reconstruction significantly improves the spatialresolution, contrast recovery, and quantitation achieved in the PETimage without compromising the statistics in the image, as is the casein gated PET approaches. It is noted that, as mentioned above, themotion information can be estimated from medical images acquired at adifferent time from the PET data, although preferably when thecorresponding motion phase of the PET events can be matched with themotion phase of the MR-derived motion. Such matching can be performedusing various modalities and techniques, including electrocardiogram(“ECG”) measures, as well as measurements using reflective trackers, MRnavigators and so forth.

In some embodiments, the PET image reconstruction can be performed usingan ordered-subset expectation maximization (“OSEM”) algorithm. Forinstance, the following updating loop can be used based on the systemmatrix of Eqn. (3):

$\begin{matrix}{{f^{{iter} + 1} = {\frac{f^{iter}}{\sum\limits_{t}^{\;}\; {D_{t}M_{t}^{T}G^{T}A_{t}{BN}\; 1_{t}}}{\sum\limits_{t}^{\;}\; {M_{t}^{T}G^{T}B\left\{ \frac{y_{t}}{{{BGM}_{t}f^{iter}} + {\left( {A_{t}N} \right)^{- 1}\left( {{\overset{\_}{s}}_{c} + \overset{\_}{r}} \right)}} \right\}}}}};} & (4)\end{matrix}$

where D_(t) is the relative duration of frame t; y_(t) is a sinogram ofsize I, which is the number of sinogram bins, containing the eventsrebinned into M motion frames; f is a vector of size J, which is thenumber of voxels, containing the voxels' radiotracer concentration; s_(c) and {tilde over (r)} contains estimated scattered and randomcoincidences, respectively, that contribute to the expected data in eachmotion frame; and 1_(t) is a column vector of size I with all ones.

PSF modeling incorporation into the system matrix can be performed toreduce partial volume effects that are caused by the limited PET spatialresolution, and which can lead to underestimation of reconstructeduptake values. Partial volume effects can be significantly reduced bymodeling the PSF of the scanner in either projection or image space, andby incorporating that information into the system matrix utilized in theimage reconstruction, as mentioned above.

As one example, PSF modeling can be performed in the image domain, whereit is straightforward to implement in a list-mode reconstruction. Toassess the resolution throughout the FOV and to compute the operator, B,in Eqn. (3), point source measurements in air can be used. For instance,0.5 mm F-18 point source measurements in air can be used.

As one non-limiting example, a for given axial position, four pointsources can be simultaneously positioned at different radial positions,each with different azimuthal angles across the scanner FOV.Coincidences for two or more different axial positions can then bemeasured. The point-source data can then be reconstructed, and thereconstructed point-source profiles fitted with Gaussian functions toextract the width parameters (a) along radial, tangent, and axialdirections. These values can then be used to obtain a for any givenpoint within the FOV by linear interpolation. This PSF information canthen be incorporated into the system matrix, as described above.

Although an OSEM reconstruction algorithm is described above, it will beappreciated by those skilled in the art that the motion, attenuation,and PSF information can also be incorporated into the system matrix usedfor any other suitable image reconstruction algorithm, including aniterative maximum likelihood expectation maximization (“MLEM”)reconstruction or a maximum a posteriori (“MAP”) reconstructionalgorithm. With a MAP reconstruction, the motion information can also beincorporated into the spatial priors to yield an accurate reconstructionof the PET activity distribution while modeling organ motion in theprojector of the reconstruction algorithm.

In addition, motion information estimated at process block 106 may beused to generate motion-dependent attenuation maps to correct forspatial mismatches between emission and attenuation data. This is animportant step because mismatches between the emission and attenuationdatasets due to motion have been shown to create artifacts in the finalPET image. Such artifacts may impair the accuracy of the diagnostic, andcurrently no techniques for correcting them in a robust and accurate wayother than as described herein.

The above-described reconstruction algorithm could be applied to anyPET-MRI scanner where the PET data is acquired simultaneously orsequentially to the MRI data. It could also be applied to any PET-CTscanner where the CT data is acquired over a sufficiently long period oftime to allow estimation of the internal and external motion. Themotion-corrected PET image(s) generated at process block 108 not onlyhave the best spatial resolution achievable, on account of correctiondue to motion, but also the lowest noise level possible since all thePET events from an acquisition step may be formulated into a singleimage, in contrast to previous techniques that include separating thePET acquisition into consecutive images containing a fraction of all PETevents. In addition, motion corrected PET image(s) generated at processblock 108 also have the desirable uniformity of image properties fortask-based applications, such as lesion detection and quantificationsince the image quality is normalized by the degree of motion at eachvoxel. In this manner motion correction may be provided for bothinternal organs as well as external motion. Finally, at process block110 a report may be generated, which may include the reconstructed PETimages in addition to any other suitable information, includinginformation about the estimated motion.

Referring now to FIG. 2, the present disclosure may be implemented usinga combined or simultaneous MR-PET system 200. The system 200 can beconceptualized as including an MRI system 400 having a cylindricalmagnet assembly 424 which receives a subject to be imaged. Disposedwithin the magnet assembly 424 is a PET system 300 that includesplurality of PET detector rings 372 supported by a cylindrical PETgantry 370. Accordingly, each detector ring 372 has an outer diameterdimensioned to be received within the geometry of the MRI system 400. Inother configurations, a single PET detector ring may be utilized. Apatient table 250 is provided to receive a patient to be imaged. Thegantry 370 is slidably mounted on the patient table 250 such that itsposition can be adjusted within the magnet assembly 424 by sliding italong the patient table 250. An RF coil 428 is employed to acquire MRsignal data from a patient and is positioned between the PET detectorrings 272 and the patient to be imaged. PET and MR data acquisitions arecarried out on the patient, either simultaneously, in an interlaced orinterleaved manner, or sequentially. Combined PET/MR imaging systemshave been described, for example, in U.S. Pat. No. 7,218,112 and in U.S.Patent Application No. 2007/0102641, which are incorporated herein byreference.

Referring particularly to FIG. 3, the PET system 300 includes the gantry370, which supports the detector ring assembly 372. The detector ring372 includes detector units 320. The signals produced by the detectorunits 320 are then received by a set of acquisition circuits 325, whichproduce digital signals indicating the line of response and the totalenergy. These signals are sent through a communications link 326 to anevent locator circuit 327. Each acquisition circuit 325 also produces anevent detection pulse (“EDP”) which indicates the exact moment thescintillation event took place.

The event locator circuits 327 form part of a data acquisition processor330, which periodically samples the signals produced by the acquisitioncircuits 325. The processor 330 has an acquisition CPU 329 whichcontrols communications on local area network 318 and a backplane bus331. The event locator circuits 327 assemble the information regardingeach valid event into a set of digital numbers that indicate preciselywhen the event took place and the position of the scintillator crystalwhich detected the event. This event data packet is conveyed to acoincidence detector 332 which is also part of the data acquisitionprocessor 330.

The coincidence detector 332 accepts the event data packets from theevent locators 327 and determines if any two of them are in coincidence.Coincidence is determined by a number of factors. First, the timemarkers in each event data packet must be within a preset time of eachother, and second, the locations indicated by the two event data packetsmust lie on a straight line. Events that cannot be paired are discarded,but coincident event pairs are located and recorded as a coincidencedata packet. As will be described, the coincidence data packets can becorrected for motion of the subject during the acquisition usinginformation received from the MRI system 400 of FIG. 4. Using thiscorrective information and the information in each coincidence datapacket, a corresponding set of corrected coincidence data packets can becalculated. As will be described, each coincidence data packet can,thus, be corrected to change its projection ray, (R, θ) by an amountcorresponding to the movement of the subject, as determined usinginformation from the MRI system 400 of FIG. 4.

The corrected coincidence data packets are conveyed through a link 333to a sorter 334 where they are used to form a sinogram. This correctiveprocess is repeated each time corrective values are received from theMRI system. The correction is made on those coincidence data packetsthat have accumulated since the receipt of the previous correctivevalues.

The sorter 334 forms part of an image reconstruction processor 340. Thesorter 334 counts all events occurring along each projection ray (R, θ)and organizes them into a two dimensional sinogram array 348 which isstored in a memory module 343. In other words, a count at sinogramlocation (R, θ) is increased each time a corrected coincidence datapacket at that projection ray is received. Due to the corrections madeto the coincidence events, the sinogram that is formed during the scandepicts the subject being examined in the reference position despitesubject motion that occurs during the scan. The image reconstructionprocessor 340 also includes an image CPU 342 that controls a backplanebus 341 and links it to the local area network 318. An array processor345 also connects to the backplane 341 and it reconstructs an image fromthe sinogram array 348. The resulting image array 346 is stored inmemory module 343 and is output by the image CPU 342 to the operatorwork station 315.

The operator work station 315 includes a CPU 350, a display 351 and akeyboard 352. The CPU 350 connects to the network 218 and it scans thekeyboard 252 for input information. Through the keyboard 352 andassociated control panel switches, the operator can control thecalibration of the PET scanner and its configuration. Similarly, theoperator can control the display of the resulting image on the display351 and perform image enhancement functions using programs executed bythe work station CPU 350.

Referring to FIG. 4, the MRI system 400 is illustrated in furtherdetail. The MRI system 400 includes an operator workstation 402, whichwill typically include a display 404; one or more input devices 406,such as a keyboard and mouse; and a processor 408. The processor 408 mayinclude a commercially available programmable machine running acommercially available operating system. The operator workstation 402provides the operator interface that enables scan prescriptions to beentered into the MRI system 400. In general, the operator workstation402 may be coupled to four servers: a pulse sequence server 410; a dataacquisition server 412; a data processing server 414; and a data storeserver 416. The operator workstation 402 and each server 410, 412, 414,and 416 are connected to communicate with each other. For example, theservers 410, 412, 414, and 416 may be connected via a communicationsystem 440, which may include any suitable network connection, whetherwired, wireless, or a combination of both. As an example, thecommunication system 440 may include both proprietary or dedicatednetworks, as well as open networks, such as the internet.

The pulse sequence server 410 functions in response to instructionsdownloaded from the operator workstation 402 to operate a gradientsystem 418 and a radiofrequency (RF) system 420. Gradient waveformsnecessary to perform the prescribed scan are produced and applied to thegradient system 418, which excites gradient coils in an assembly 422 toproduce the magnetic field gradients G_(x), G_(y), and G_(z) used forposition encoding magnetic resonance signals. The gradient coil assembly422 forms part of a magnet assembly 424 that includes a polarizingmagnet 426 and optionally a whole-body RF coil 428.

RF waveforms are applied by the RF system 420 to the RF coil 428, inorder to perform the prescribed magnetic resonance pulse sequence.Responsive magnetic resonance signals detected by the RF coil 428 arereceived by the RF system 420, where they are amplified, demodulated,filtered, and digitized under direction of commands produced by thepulse sequence server 410. The RF system 420 includes an RF transmitterfor producing a wide variety of RF pulses used in MRI pulse sequences.The RF transmitter is responsive to the scan prescription and directionfrom the pulse sequence server 410 to produce RF pulses of the desiredfrequency, phase, and pulse amplitude waveform. The generated RF pulsesmay be applied to the whole-body RF coil 428 or to one or more localcoils or coil array.

The RF system 420 also includes one or more RF receiver channels. EachRF receiver channel includes an RF preamplifier that amplifies themagnetic resonance signal received by the coil 428 or by the local coilor coil array to which it is connected, and a detector that detects anddigitizes the I and Q quadrature components of the received magneticresonance signal. The magnitude of the received magnetic resonancesignal may, therefore, be determined at any sampled point by the squareroot of the sum of the squares of the I and Q components M=√{square rootover (I²+Q²)} and the phase of the received magnetic resonance signalmay also be determined according to the following relationship

$\phi = {{\tan^{- 1}\left( \frac{Q}{I} \right)}.}$

The pulse sequence server 410 also optionally receives patient data froma physiological acquisition controller 430. By way of example, thephysiological acquisition controller 430 may receive signals from anumber of different sensors connected to the patient, such aselectrocardiograph (ECG) signals from electrodes, or respiratory signalsfrom a respiratory bellows or other respiratory monitoring device. Suchsignals are typically used by the pulse sequence server 410 tosynchronize, or “gate,” the performance of the scan with the subject'sheart beat or respiration.

The pulse sequence server 410 also connects to a scan room interfacecircuit 432 that receives signals from various sensors associated withthe condition of the patient and the magnet system. It is also throughthe scan room interface circuit 432 that a patient positioning system434 receives commands to move the patient to desired positions duringthe scan.

The digitized magnetic resonance signal samples produced by the RFsystem 420 are received by the data acquisition server 412. The dataacquisition server 412 operates in response to instructions downloadedfrom the operator workstation 402 to receive the real-time magneticresonance data and provide buffer storage, such that no data is lost bydata overrun. In some scans, the data acquisition server 412 does littlemore than pass the acquired magnetic resonance data to the dataprocessor server 414. However, in scans that require information derivedfrom acquired magnetic resonance data to control the further performanceof the scan, the data acquisition server 412 is programmed to producesuch information and convey it to the pulse sequence server 410. Forexample, during prescans, magnetic resonance data is acquired and usedto calibrate the pulse sequence performed by the pulse sequence server410. As another example, navigator signals may be acquired and used toadjust the operating parameters of the RF system 420 or the gradientsystem 418, or to control the view order in which k-space is sampled. Instill another example, the data acquisition server 412 may also beemployed to process magnetic resonance signals used to determine patientmotion, and communicate such to the PET system 300 described withrespect to FIG. 3 to perform motion correction or compensation. By wayof example, the data acquisition server 412 acquires magnetic resonancedata and processes it in real-time to produce information that is usedto control the overall operation of the MR and PET imaging acquisitions.

The data processing server 414 receives magnetic resonance data from thedata acquisition server 412 and processes it in accordance withinstructions downloaded from the operator workstation 402. Suchprocessing may, for example, include one or more of the following:reconstructing two-dimensional or three-dimensional images by performinga Fourier transformation of raw k-space data; performing other imagereconstruction algorithms, such as iterative or backprojectionreconstruction algorithms; applying filters to raw k-space data or toreconstructed images; generating functional magnetic resonance images;calculating motion or flow images; and so on.

Images reconstructed by the data processing server 414 are conveyed backto the operator workstation 402 where they are stored. Real-time imagesare stored in a data base memory cache (not shown in FIG. 4), from whichthey may be output to operator display 412 or a display 436 that islocated near the magnet assembly 424 for use by attending physicians.Batch mode images or selected real time images are stored in a hostdatabase on disc storage 438. When such images have been reconstructedand transferred to storage, the data processing server 414 notifies thedata store server 416 on the operator workstation 402. The operatorworkstation 402 may be used by an operator to archive the images,produce films, or send the images via a network to other facilities.

The MRI system 400 may also include one or more networked workstations442. By way of example, a networked workstation 442 may include adisplay 444; one or more input devices 446, such as a keyboard andmouse; and a processor 448. The networked workstation 442 may be locatedwithin the same facility as the operator workstation 402, or in adifferent facility, such as a different healthcare institution orclinic.

The networked workstation 442, whether within the same facility or in adifferent facility as the operator workstation 402, may gain remoteaccess to the data processing server 414 or data store server 416 viathe communication system 440. Accordingly, multiple networkedworkstations 442 may have access to the data processing server 414 andthe data store server 416. In this manner, magnetic resonance data,reconstructed images, or other data may exchanged between the dataprocessing server 414 or the data store server 416 and the networkedworkstations 442, such that the data or images may be remotely processedby a networked workstation 442. This data may be exchanged in anysuitable format, such as in accordance with the transmission controlprotocol (TCP), the internet protocol (IP), or other known or suitableprotocols.

The present invention has been described in terms of one or morepreferred embodiments, and it should be appreciated that manyequivalents, alternatives, variations, and modifications, aside fromthose expressly stated, are possible and within the scope of theinvention.

1. A method for compensating motion artifacts in positron emissiontomography (“PET”) imaging based on medical images acquired with amedical imaging system, the method comprising: a) acquiring PET datafrom a subject with a PET system during which motion of at least aportion of the subject is occurring; b) providing medical imagesacquired from the subject using a medical imaging system, the medicalimages including regions depicting motion; c) estimating, from themedical images, motion information indicative of the motion of the atleast a portion of the subject that occurred in step a); and d)reconstructing a motion-corrected PET image using the PET data and areconstruction algorithm that incorporates the estimated motioninformation into a system matrix.
 2. The method of claim 1, wherein themotion includes at least one of a cardiac motion, a respiratory motion,and a non-periodic motion.
 3. The method of claim 1, wherein step b)includes acquiring the medical images using at least one of a magneticresonance imaging (MRI) system and a computed tomography (CT) system. 4.The method of claim 3, wherein the medical imaging system and the PETsystem form an integrated system.
 5. The method of claim 1, whereinestimating the motion information at step (c) includes minimizing a costfunction defined as:Φ=M(f _(T) ,Tf _(S))+R(T) wherein f_(T) and f_(S) are a target and asource image, respectively, T is a motion operator, M is a similaritymeasure, R(T) is a regularizer and β is a regularization parameter. 6.The method of claim 5, wherein the similarity measure includes one of asum of squared difference and a mutual information parameter.
 7. Themethod of claim 1, wherein the reconstruction algorithm includes anordered-subset expectation maximization (“OSEM”) reconstruction.
 8. Themethod of claim 1, wherein the reconstruction algorithm includes aniterative maximum likelihood expectation maximization (“MLEM”)reconstruction.
 9. The method of claim 1, wherein the reconstructionalgorithm includes a maximum a posteriori (“MAP”) reconstructionalgorithm with the motion information further incorporated into spatialpriors.
 10. The method of claim 1, further comprising providing a modelof a point-spread function (PSF) for the PET system and wherein step d)includes incorporating the model of the PSF into the system matrix ofthe reconstruction algorithm.
 11. The method of claim 1, wherein themedical images are magnetic resonance images acquired using a pulsesequence that tags motion.
 12. The method of claim 1, wherein themedical images are magnetic resonance images acquired using a pulsesequence that also acquires navigator data, and the motion estimated instep c) is estimated from the navigator data.